用多重拓扑分割空间，提升房贷审批预测准确率
Binary AddiVortes: (Bayesian) Additive Voronoi Tessellations for Binary Classification with an application to Predicting Home Mortgage Application Outcomes
- 通过潜变量与阈值法将拓扑分割模型用于二分类
- 在多个指标上优于随机森林和贝叶斯加性回归树
- 适合需要可解释决策的金融风控场景
Additive Voronoi Tessellations（AddiVortes）是一种多变量回归模型，利用多个Voronoi剖分对协变量空间进行划分，构建可加集成模型。本文将该框架扩展至二分类任务，引入基于潜变量的probit模型，并采用数据增强技术，通过阈值判定二元响应。在多种评估指标下，AddiVortes在多数情况下表现优于随机森林、贝叶斯加性回归树（BART）及其他主流黑箱回归模型。研究以真实房贷申请数据为背景，基于多种协变量分析个人获得房贷审批的概率，验证了模型捕捉复杂数据关系的能力，展示了其在优化房贷审批决策中的潜力。
原文摘要 · Abstract (English)
The Additive Voronoi Tessellations (AddiVortes) model is a multivariate regression model that uses multiple Voronoi tessellations to partition the covariate space for an additive ensemble model. In this paper, the AddiVortes framework is extended to binary classification by incorporating a probit model with a latent variable formulation. Specifically, we utilise a data augmentation technique, where a latent variable is introduced and the binary response is determined via thresholding. In most cases, the AddiVortes model outperforms random forests, BART and other leading black-box regression models when compared using a range of metrics. A comprehensive analysis is conducted using AddiVortes to predict an individual's likelihood of being approved for a home mortgage, based on a range of covariates. This evaluation highlights the model's effectiveness in capturing complex relationships within the data and its potential for improving decision-making in mortgage approval processes.
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